DataScienceEngineering/5-MapsAndProviders

1import gradio as gr2import pandas as pd3import plotly.graph_objects as go4from datasets import load_dataset5 6dataset = load_dataset('text', data_files={'train': ['NPI_2023_01_17-05.10.57.PM.csv'], 'test': 'NPI_2023_01_17-05.10.57.PM.csv'})7#1.6GB NPI file with MH therapy taxonomy provider codes (NUCC based) with human friendly replacement labels (e.g. Counselor rather than code)8datasetNYC = load_dataset("gradio/NYC-Airbnb-Open-Data", split="train")9df = datasetNYC.to_pandas()10 11def MatchText(pddf, name):12    pd.set_option("display.max_rows", None)13    data = pddf14    swith=data.loc[data['text'].str.contains(name, case=False, na=False)]15    return swith16 17def getDatasetFind(findString):18    #finder = dataset.filter(lambda example: example['text'].find(findString))19    finder = dataset['train'].filter(lambda example: example['text'].find(findString))20    finder = finder = finder.to_pandas()21    g1=MatchText(finder, findString)22    return g123 24def filter_map(min_price, max_price, boroughs):25    filtered_df = df[(df['neighbourhood_group'].isin(boroughs)) & (df['price'] > min_price) & (df['price'] < max_price)]26    names = filtered_df["name"].tolist()27    prices = filtered_df["price"].tolist()28    text_list = [(names[i], prices[i]) for i in range(0, len(names))]29    30    fig = go.Figure(go.Scattermapbox(31            customdata=text_list,32            lat=filtered_df['latitude'].tolist(),33            lon=filtered_df['longitude'].tolist(),34            mode='markers',35            marker=go.scattermapbox.Marker(36                size=637            ),38            hoverinfo="text",39            hovertemplate='Name: %{customdata[0]}Price: $%{customdata[1]}'40        ))41 42    fig.update_layout(43        mapbox_style="open-street-map",44        hovermode='closest',45        mapbox=dict(46            bearing=0,47            center=go.layout.mapbox.Center(48                lat=40.67,49                lon=-73.9050            ),51            pitch=0,52            zoom=953        ),54    )55    return fig56 57def centerMap(min_price, max_price, boroughs):58    filtered_df = df[(df['neighbourhood_group'].isin(boroughs)) & (df['price'] > min_price) & (df['price'] < max_price)]59    names = filtered_df["name"].tolist()60    prices = filtered_df["price"].tolist()61    text_list = [(names[i], prices[i]) for i in range(0, len(names))]62    63    latitude = 44.938264    longitude = -93.656165    66    fig = go.Figure(go.Scattermapbox(67            customdata=text_list,68            lat=filtered_df['latitude'].tolist(),69            lon=filtered_df['longitude'].tolist(),            mode='markers',70            marker=go.scattermapbox.Marker(71                size=672            ),73            hoverinfo="text",74            #hovertemplate='Lat: %{lat} Long:%{lng} City: %{cityNm}'75        ))76 77    fig.update_layout(78        mapbox_style="open-street-map",79        hovermode='closest',80        mapbox=dict(81            bearing=0,82            center=go.layout.mapbox.Center(83                lat=latitude,84                lon=longitude85            ),86            pitch=0,87            zoom=988        ),89    )90    return fig91 92 93with gr.Blocks() as demo:94    with gr.Column():95        96        # Price/Boroughs/Map/Filter for AirBnB97        with gr.Row():98            min_price = gr.Number(value=250, label="Minimum Price")99            max_price = gr.Number(value=1000, label="Maximum Price")100        boroughs = gr.CheckboxGroup(choices=["Queens", "Brooklyn", "Manhattan", "Bronx", "Staten Island"], value=["Queens", "Brooklyn"], label="Select Boroughs:")101        btn = gr.Button(value="Update Filter")102        map = gr.Plot().style()103        104        # Mental Health Provider Finder105        with gr.Row():106            df20 = gr.Textbox(lines=4, default="", label="Find Mental Health Provider e.g. City/State/Name/License:")107            btn2 = gr.Button(value="Find")108        with gr.Row():109            df4 = gr.Dataframe(wrap=True, max_rows=10000, overflow_row_behaviour= "paginate")110 111        # City Map112        with gr.Row():113            df2 = gr.Textbox(lines=1, default="Mound", label="Find City:")114            latitudeUI = gr.Textbox(lines=1, default="44.9382", label="Latitude:")115            longitudeUI = gr.Textbox(lines=1, default="-93.6561", label="Longitude:")116            btn3 = gr.Button(value="Lat-Long")117 118    demo.load(filter_map, [min_price, max_price, boroughs], map)119    120    btn.click(filter_map, [min_price, max_price, boroughs], map)121    btn2.click(getDatasetFind,df20,df4 )122    # Lookup on US once you have city to get lat/long123    # US	55364	Mound	Minnesota	MN	Hennepin	053			44.9382	-93.6561	4124    #latitude = 44.9382125    #longitude = -93.6561126    #btn3.click(centerMap, map)127        128    btn3.click(centerMap, [min_price, max_price, boroughs], map)129 130demo.launch()